Guanghua Zhang, Mengnan Qu, Yuhao Jin, and Qingpeng Song

School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China(Submitted on October 24, 2018; Revised on November 26, 2018; Accepted on December 28, 2018)

Abstract:

Coloring animation sketches has always been a complex and interesting task, but as the sketch is the first part of animation creation that neither presents gray value nor presents semantic information, the lack of real animation sketches is the biggest difficulty in current model training. It is also usually expensive to collect such data. In recent years, some methods based on generative adversarial networks (GANs) have achieved great success. They can generate colorized anime illustration on given sketches. Many existing sketch coloring tools are based on this supervised learning method, but the marking of data is particularly important for supervised learning, and much time is spent on the marking of data. To address these challenges, we propose a novel approach for unsupervised learning based on U-net and periodic consistent confrontation. Specifically, we combine the periodic consistent antagonism framework with the U-net structure and residual network, enabling us to robustly train a deep network to make the resulting images more natural and realistic. We also adopted some special data generation methods, so that our model can not only color anime sketches but also extract line drafts from colored pictures. By comparing the mainstream models of supervised learning, we show that the image processed by the proposed method can achieve a similar effect.